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Micromachines
Special Issues
Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems
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Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 14541

Special Issue Editors

Dr. Jianxiong Zhu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: human interface sensor; self-powered sensor; wearable sensor; digital twin; data confusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhisheng Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing, China
Interests: intelligent manufacturing; data fusion; machine vision; quality control
Dr. Haiying Wen

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing, China
Interests: parallel robotics; bio-inspired robot; robot optimization design and control

Special Issue Information

Dear Colleagues,

Smart sensing and advanced systems play an important role today, especially the application of sensing in the IoT and with the aid of artificial intelligence. There are plenty of sensor methodologies for various applications, such as in industrial areas, chemical areas, robotics, and sustainable systems. The methodologies contain piezoelectric, piezoresisitve, triboelectric, magnetic, optics, ions, and chemiresisitve. With the help of sensors in all kinds of applications, intelligent process and manufacturing technologies have been on the receiving end of significant research efforts from numerous research groups across the world, which have enabled machine learning algorithms and enhanced calculated performance and multisensor-based intelligent process control systems, such as smart homes, smart factory, decision support system, robotics, etc. 

This Special Issue seeks to showcase research papers and review articles in this field and welcomes contributions devoted to the micro/nanofabrication, methodology, integration, and application of artificial intelligence and smart sensors and advanced industrial process systems, with a particular interest in multisensor fusion, machine vision technologies, digital twin technologies, the human–machine interface, virtual reality, machine learning, big data, advanced robotics, and other applications. Topics of interest include but are not limited to: 

  • All kinds of advanced sensing methodologies;
  • The fabrication and materials in sensors;
  • Machine learning and multisensor-based industrial process technology;
  • Machine-vision-based industrial defect detection;
  • Artificial-intelligence-assisted industrial process failure prediction;
  • Digital twin application in industrial advanced technology;
  • Robotics and intelligent manufacturing application;
  • Innovative human–machine interaction for intelligent manufacturing.

Dr. Jianxiong Zhu
Prof. Dr. Zhisheng Zhang
Dr. Haiying Wen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • smart sensor
  • multisensor fusion
  • machine vision
  • digital twin
  • intelligent manufacturing
  • advanced robotics

Published Papers (6 papers)

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Research

15 pages, 4706 KiB  
Article
Experimental Investigation of Vibration Isolator for Large Aperture Electromagnetic MEMS Micromirror
by Lei Qian, Yameng Shan, Junduo Wang, Haoxiang Li, Kewei Wang, Huijun Yu, Peng Zhou and Wenjiang Shen
Micromachines 2023, 14(8), 1490; https://doi.org/10.3390/mi14081490 - 25 Jul 2023
Cited by 2 | Viewed by 1093
Abstract
The Micro-Electro-Mechanical-System (MEMS) micromirror has shown great advantages in Light Detection and Ranging (LiDAR) for autonomous vehicles. The equipment on vehicles is usually exposed to environmental vibration that may degrade or even destroy the flexure of the micromirror for its delicate structure. In [...] Read more.
The Micro-Electro-Mechanical-System (MEMS) micromirror has shown great advantages in Light Detection and Ranging (LiDAR) for autonomous vehicles. The equipment on vehicles is usually exposed to environmental vibration that may degrade or even destroy the flexure of the micromirror for its delicate structure. In this work, a mechanical low-pass filter (LPF) acting as a vibration isolator for a micromirror is proposed. The research starts with the evaluation of vibration influences on the micromirror by theoretical calculation and simulation. The results illustrate that mechanical load concentrates at the slow flexure of the micromirror as it is excited to resonate in second-order mode (named piston mode) in Z-direction vibration. A specific LPF for the micromirror is designed to attenuate the response to high-frequency vibration, especially around piston mode. The material of the LPF is a beryllium-copper alloy, chosen for its outstanding properties of elasticity, ductility, and fatigue resistance. To measure the mechanical load on the micromirror in practical, the on-chip piezoresistive sensor is utilized and a relevant test setup is built to validate the effect of the LPF. Micromirrors with or without the LPF are both tested under 10 g vibration in the Z-direction. The sensor output of the device with the LPF is 35.9 mV in piston mode, while the device without the LPF is 70.42 mV. The attenuation ratio is 0.51. This result demonstrates that the LPF structure can effectively reduce the stress caused by piston mode vibration. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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13 pages, 2819 KiB  
Article
Self-Attention-Augmented Generative Adversarial Networks for Data-Driven Modeling of Nanoscale Coating Manufacturing
by Shanling Ji, Jianxiong Zhu, Yuan Yang, Hui Zhang, Zhihao Zhang, Zhijie Xia and Zhisheng Zhang
Micromachines 2022, 13(6), 847; https://doi.org/10.3390/mi13060847 - 29 May 2022
Cited by 3 | Viewed by 1953
Abstract
Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an [...] Read more.
Nanoscale coating manufacturing (NCM) process modeling is an important way to monitor and modulate coating quality. The multivariable prediction of coated film and the data augmentation of the NCM process are two common issues in smart factories. However, there has not been an artificial intelligence model to solve these two problems simultaneously. Focusing on the two problems, a novel auxiliary regression using a self-attention-augmented generative adversarial network (AR-SAGAN) is proposed in this paper. This model deals with the problem of NCM process modeling with three steps. First, the AR-SAGAN structure was established and composed of a generator, feature extractor, discriminator, and regressor. Second, the nanoscale coating quality was estimated by putting online control parameters into the feature extractor and regressor. Third, the control parameters in the recipes were generated using preset parameters and target quality. Finally, the proposed method was verified by the experiments of a solar cell antireflection coating dataset, the results of which showed that our method performs excellently for both multivariable quality prediction and data augmentation. The mean squared error of the predicted thickness was about 1.6~2.1 nm, which is lower than other traditional methods. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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14 pages, 2402 KiB  
Article
Measuring Liquid Droplet Size in Two-Phase Nozzle Flow Employing Numerical and Experimental Analyses
by Lin Jiang, Wei Rao, Lei Deng, Atilla Incecik, Grzegorz Królczyk and Zhixiong Li
Micromachines 2022, 13(5), 684; https://doi.org/10.3390/mi13050684 - 27 Apr 2022
Viewed by 1549
Abstract
The flavoring process ensures the quality of cigarettes by endowing them with special tastes. In this process, the flavoring liquid is atomized into particles by a nozzle and mixed with the tobacco in a rotating drum. The particle size of the flavoring liquid [...] Read more.
The flavoring process ensures the quality of cigarettes by endowing them with special tastes. In this process, the flavoring liquid is atomized into particles by a nozzle and mixed with the tobacco in a rotating drum. The particle size of the flavoring liquid has great influence on the atomization effect; however, limited research has addressed the quantitation of the liquid particle size in two-phase nozzle flow. To bridge this research gap, the authors of this study employed numerical and experimental techniques to explore the quantitative analysis of particle size. First, a simulation model for the flavoring nozzle was established to investigate the atomization effect under different ejection pressures. Then, an experimental test is carried out to compare the test results with the simulation results. Lastly, the influencing factors of liquid particle size in two-phase nozzle flow were analyzed to quantify particle size. The analysis results demonstrated that there was a cubic correction relationship between the simulation and experiment particle size. The findings of this study may provide a reliable reference when evaluating the atomization effect of flavoring nozzles. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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25 pages, 7400 KiB  
Article
Kinetic Walking Energy Harvester Design for a Wearable Bowden Cable-Actuated Exoskeleton Robot
by Yunde Shi, Mingqiu Guo, Heran Zhong, Xiaoqiang Ji, Dan Xia, Xiang Luo and Yuan Yang
Micromachines 2022, 13(4), 571; https://doi.org/10.3390/mi13040571 - 03 Apr 2022
Cited by 10 | Viewed by 5276
Abstract
Over the past few decades, wearable exoskeletons of various forms have been developed to assist human activities or for rehabilitation of movement disorders. However, sustainable exoskeletons with efficient energy harvesting devices still have not been fully explored. In this paper, we propose the [...] Read more.
Over the past few decades, wearable exoskeletons of various forms have been developed to assist human activities or for rehabilitation of movement disorders. However, sustainable exoskeletons with efficient energy harvesting devices still have not been fully explored. In this paper, we propose the design of a lightweight wearable Bowden-cable-actuated soft exoskeleton robot with energy harvesting capability. Unlike previous wearable exoskeletons, the presented exoskeleton uses an electromagnetic generator to both harvest biomechanical energy and to output mechanical torque by controlling an operation mode relay switch based on a human’s gait. Moreover, the energy-harvesting module also acts as a knee impact absorber for the human, where the effective damping level can be modulated in a controlled manner. The harvested energy is regulated and stored in super capacitors for powering wireless sensory devices when needed. The experimental results show an average of a 7.91% reduction in thigh muscle activity, with a maximum of 3.2 W of electric power being generated during movement downstairs. The proposed design offers important prospects for the realization of lightweight wearable exoskeletons with improved efficiency and long-term sustainability. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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12 pages, 2759 KiB  
Article
Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules
by Jiachuan Yu, Yuan Yang, Hui Zhang, Han Sun, Zhisheng Zhang, Zhijie Xia, Jianxiong Zhu, Min Dai and Haiying Wen
Micromachines 2022, 13(2), 332; https://doi.org/10.3390/mi13020332 - 19 Feb 2022
Cited by 8 | Viewed by 1894
Abstract
Electroluminescence (EL) imaging is a widely adopted method in quality assurance of the photovoltaic (PV) manufacturing industry. With the growing demand for high-quality PV products, automatic inspection methods based on machine vision have become an emerging area concern to replace manual inspectors. Therefore, [...] Read more.
Electroluminescence (EL) imaging is a widely adopted method in quality assurance of the photovoltaic (PV) manufacturing industry. With the growing demand for high-quality PV products, automatic inspection methods based on machine vision have become an emerging area concern to replace manual inspectors. Therefore, this paper presents an automatic defect-inspection method for multi-cell monocrystalline PV modules with EL images. A processing routine is designed to extract the defect features of the PV module, eliminating the influence of the intrinsic structural features. Spectrum domain analysis is applied to effectively reconstruct an improved PV layout from a defective one by spectrum filtering in a certain direction. The reconstructed image is used to segment the PV module into cells and slices. Based on the segmentation, defect detection is carried out on individual cells or slices to detect cracks, breaks, and speckles. Robust performance has been achieved from experiments on many samples with varying illumination conditions and defect shapes/sizes, which shows the proposed method can efficiently distinguish intrinsic structural features from the defect features, enabling precise and speedy defect detections on multi-cell PV modules. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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12 pages, 3742 KiB  
Article
Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling
by Han Sun, Yuan Yang, Jiachuan Yu, Zhisheng Zhang, Zhijie Xia, Jianxiong Zhu and Hui Zhang
Micromachines 2022, 13(2), 300; https://doi.org/10.3390/mi13020300 - 14 Feb 2022
Cited by 5 | Viewed by 1635
Abstract
Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health [...] Read more.
Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics. Full article
(This article belongs to the Special Issue Methodology, Microfabrication and Applications of Advanced Sensing and Smart Systems)
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